Background

In the natural world, there is a phenomenal variety of different eye
designs --- high resolution, camera-type eyes consisting of a lens and
a retina; lower resolution
insect eyes
made up of thousands of nearly
identical components; and the exotic eyes of the
stalk-eyed flies, or the
stomatopods.
This biological diversity has been mimicked in the
computer vision community --- the past decade has brought the
introduction of very wide angle cameras,
catadioptic and omnidirectional cameras,
multi-camera networks, and
plenoptic cameras.
Because these different camera
designs sample the local visual field differently, they are suited to
different tasks and environments.

Current Work

I am exploring the extremes of the space of camera designs. Below is
one example, the "fiber optic camera". This camera (and nearly all
new camera designs) fit within the generalized imaging model proposed
recently by Grossberg and Nayar. The drawbacks of this camera design
are (1) that it is time consuming (not difficult, just time consuming)
to calibrate, and (2) that since it does not approximate a central
projection (pinhole camera type model), new algorithms are necessary
for the motion estimation process. The ego-motion constraints are
defined in a paper presented at Omnivis 2002.

These ego-motion constraint define an error function. This function
is minimized to determing the ego-motion parameters. This problem is
of the same form for all different camera varieties. Considering the
Fisher Information Matrix of this error function makes it possible to
give quantitative comparisons of different camera designs in
terms of their ability to estimate ego-motion. It also gives a mechanism for
optimizing the design of a camera system for a particular environment
--- or reverse engineering biological eye designs to determine for
what environment they are adapted.

This "Fiber Optic Camera" is one extreme of a highly non-standard
mechanism for sampling the visual field. This camera can sample the
visual field either as a uniform sampling of a hemisphere,

or, in other sampling patterns, which may be optimal if there is prior
knowledge of the possible parameters of the motion, (in this case a
robot that can only turn around the vertical axis and move straight
forward).

Here is the picture of the bottom of the fiber optic bundle, the
camera captures this image, but only need to report the intensity of
each fiber optic block, because each fiber strand has (effectively)
constant intensity.